Improved Region Growing Method for Magnetic Resonance Images (MRIs) Segmentation

Abstract

Segmentation of magnetic resonance images (MRIs) is challenging due to the poor image contrast and artifacts that result in missing tissue boundaries, i.e. pixels inside the region and on the boundaries have similar intensity. In this paper, we adapt a region growing method to segment MRIs which contain weak boundaries between different tissues. The proposed region growing algorithm is developed to learn its homogeneity criterion automatically from characteristics of the region to be segmented. An automatic homogeneity criterion based on estimating probability of pixel intensities of a given image is described. The homogeneity criterions as well as the probability are calculated for each pixel. The proposed algorithm selects the pixels sequentially in a random walk starting at the seed point, and the homogeneity criterion is updated continuously. The proposed algorithm is applied to challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI) datasets. The experimental results show that the proposed technique produces accurate and stable results.